Sign Language Interpreter Aptitude
03 - Signing/Interpreting Predictors Analysis
Freya Watkins
University of Birmingham04 April 2022
Setup
Load packages
library(here) # file organisation & folder location
library(tidyverse) # data wrangling & plotting
library(scales) # scales on plots
library(lme4) # for linear mixed models
library(psych) # for factor analysisR / package versions used
R.Version() ## $platform
## [1] "x86_64-w64-mingw32"
##
## $arch
## [1] "x86_64"
##
## $os
## [1] "mingw32"
##
## $system
## [1] "x86_64, mingw32"
##
## $status
## [1] ""
##
## $major
## [1] "4"
##
## $minor
## [1] "0.5"
##
## $year
## [1] "2021"
##
## $month
## [1] "03"
##
## $day
## [1] "31"
##
## $`svn rev`
## [1] "80133"
##
## $language
## [1] "R"
##
## $version.string
## [1] "R version 4.0.5 (2021-03-31)"
##
## $nickname
## [1] "Shake and Throw"packageVersion('here')## [1] '1.0.1'packageVersion('tidyverse')## [1] '1.3.1'packageVersion('scales') ## [1] '1.1.1'packageVersion('lme4') ## [1] '1.1.27.1'packageVersion('psych')## [1] '2.1.6'Read pre-wrangled data
(see 00-wrangling-setup.Rmd script)
# here::here()
apt <- readRDS(here("data", "apt-data.rds"))
apt <- as_tibble(apt)
head(apt)## # A tibble: 6 x 46
## session group ppt status selected continue L1_bsl prior_BSL age_s1
## <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 pre-degree pilot HW002 rejected 0 0 NA NA NA
## 2 pre-degree pilot HW004 rejected 0 0 NA NA NA
## 3 pre-degree pilot HW009 rejected 0 0 NA NA NA
## 4 pre-degree pilot HW011 rejected 0 0 NA NA NA
## 5 pre-degree pilot HW012 rejected 0 0 NA NA NA
## 6 pre-degree pilot HW014 rejected 0 0 NA NA NA
## # ... with 37 more variables: nback_lett <dbl>, nback_spat <dbl>,
## # nback_comb <dbl>, corsi_bspan <dbl>, corsi_score <dbl>, corsi_corr <dbl>,
## # corsi_mspan <dbl>, kirk_ceil <dbl>, kirk_raw <dbl>, kirk_acc <dbl>,
## # kbit_ceil <dbl>, kbit_raw <dbl>, kbit_acc <dbl>, dspan_mem <dbl>,
## # dspan_corr <dbl>, dspan_time <dbl>, mr2d_acc <dbl>, mr2d_rt <dbl>,
## # mr2d_sats <dbl>, mr3d_acc <dbl>, mr3d_rt <dbl>, mr3d_sats <dbl>,
## # bis_tot <dbl>, bis_att <dbl>, bis_mot <dbl>, bis_nplan <dbl>, ...Filter out participants who did not progress beyond interview
apt <- apt %>% filter(status != "rejected")Convert data from long format to wide format
apt_wide <- apt %>%
select(-comments) %>%
tidyr::pivot_wider(names_from = session,
values_from = c(nback_lett:grade_terp))Plots & correlations
BSL: predicted impact
Corsi Blocks
vs BSL Grades
# Initial visuospatial skill vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Initial visuospatial skill vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "Initial Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000122# Initial visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Initial visuospatial skill vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "Initial Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000427# 1st year visuospatial skill vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "1st year visuospatial skill vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "1st year Corsi Blocks score ")
apt_wide %>%
filter(`corsi_corr_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.129# 1st year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "1st year visuospatial skill vs. 2nd year BSL grades",
y = "1st year BSL grade",
x = "2nd year Corsi Blocks score ")
apt_wide %>%
filter(`corsi_corr_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000223# 2nd year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2nd year visuospatial skill vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "2nd year Corsi Blocks score ")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00871vs BSL-SRT
Does Corsi Blocks performance relate to BSL Sentence Reproduction Task?
# Initial Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Initial Corsi Blocks score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "Initial Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.101# 2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "2nd year Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.609# 3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `corsi_corr_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.3012D Mental Rotation
vs BSL Grades
Does 2D Mental Rotation performance relate to BSL grades?
# Initial 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .3) +
theme_minimal() +
labs(title = "Initial 2D Mental Rotation score vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "Initial 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.347# Initial 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .3) +
geom_point() +
theme_minimal() +
labs(title = "Initial 2D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "Initial 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00131# 1st year 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .3) +
theme_minimal() +
labs(title = "1st year 2D Mental Rotation score vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "1st year 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.664# 1st year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .3) +
theme_minimal() +
labs(title = "1st year 2D Mental Rotation score vs. 2nd year BSL grades",
y = "1st year BSL grade",
x = "2nd year 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.119# 2nd year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .3) +
theme_minimal() +
labs(title = "2nd year 2D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "2nd year 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.265vs BSL-SRT
Does 2D Mental Rotation performance relate to BSL Sentence Reproduction Task?
# 2nd year 2D Mental Rotation score vs. 2nd year BSL-SRT scores
# just 4 data points here
# 3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.1913D Mental Rotation
vs BSL Grades
Does 3D Mental Rotation performance relate to BSL grades?
# Initial 3D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Initial 3D Mental Rotation score vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "Initial 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0671# Initial 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Initial 3D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "Initial 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000282# 2nd year 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2nd year 3D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "2nd year 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.386vs BSL-SRT
Does 3D Mental Rotation performance relate to BSL Sentence Reproduction Task?
# Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "Initial 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.369# 2nd year 3D Mental Rotation score vs. 2nd year BSL-SRT scores
# only 4 data points here
# 3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.123MLAT Number Learning
vs BSL Grades
# Initial MLAT Number Learning Accuracy vs. BSL grades 1st year
apt_wide %>%
filter(`mlat_acc_pre-degree` != "na") %>%
filter(`grade_bsl_1st year` != "na") %>%
ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 1st year",
x = "MLAT Number Learning Accuracy")
apt_wide %>%
filter(`mlat_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mlat_acc_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0215# Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year
apt_wide %>%
filter(`mlat_acc_pre-degree` != "na") %>%
filter(`grade_bsl_2nd year` != "na") %>%
ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year",
x = "MLAT Number Learning Accuracy")
apt_wide %>%
filter(`mlat_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mlat_acc_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0333vs BSL-SRT
# Initial MLAT Number Learning Accuracy vs. BSL-SRT 3rd year
apt_wide %>%
filter(`mlat_acc_pre-degree` != "na") %>%
filter(`bsl_srt_3rd year` != "na") %>%
ggplot(aes(x = `mlat_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Initial MLAT Number Learning Accuracy vs. BSL-SRT score 3rd year",
x = "MLAT Number Learning Accuracy")
apt_wide %>%
filter(`mlat_acc_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mlat_acc_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000354BSL: no predicted impact
Kirklees Sentence Reading
vs BSL Grades
# Kirklees Sentence Reading pre-degree vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 1st year", y = "1st year BSL grade", x = "Kirklees Sentence Reading pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.187# Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Kirklees Sentence Reading pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0116# Kirklees Sentence Reading 1st year vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 1st year",
y = "1st year BSL grade", x = "Kirklees Sentence Reading after 1 year")
apt_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.116# Kirklees Sentence Reading 1st year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Kirklees Sentence Reading after 1 year")
apt_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00827# Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Kirklees Sentence Reading 2nd year")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00824vs BSL-SRT
Does Kirklees Sentence Reading performance relate to BSL Sentence Reproduction Task?
# Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "Initial Kirklees Sentence Reading score")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.237# 2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "2nd year Kirklees Sentence Reading score")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.149KBIT-2 Matrices
vs BSL Grades
# KBIT-2 Matrices pre-degree vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 1st year",
y = "1st year BSL grade", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0140# KBIT-2 Matrices pre-degree vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00126# KBIT-2 Matrices 1st year vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 1st year",
y = "1st year BSL grade", x = "KBIT-2 Matrices 1st year")
apt_wide %>%
filter(`kbit_acc_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00195# KBIT-2 Matrices 1st year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "KBIT-2 Matrices 1st year")
apt_wide %>%
filter(`kbit_acc_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0305# KBIT-2 Matrices 2nd year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices 2nd year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "KBIT-2 Matrices 2nd year")
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.167vs BSL-SRT
Does KBIT-2 Matrices performance relate to BSL Sentence Reproduction Task?
# Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "Initial KBIT-2 Matrices score")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00494# 2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "2nd year KBIT-2 Matrices score")
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0933Dual N-Back
vs BSL Grades
# Dual N-Back pre-degree vs. BSL Grades 1st year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back pre-degree vs. BSL Grades 1st year",
y = "1st year BSL grade", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0288# Dual N-Back pre-degree vs. BSL Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = " Dual N-Back pre-degree vs. BSL Grades 2nd year",
y = "2nd year BSL grade", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00637# Dual N-Back 2nd year vs. BSL Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back 2nd year vs. BSL Grades 2nd year",
y = "2nd year BSL grade", x = "Dual N-Back 2nd year")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0520vs BSL-SRT
Does Dual N-Back performance relate to BSL Sentence Reproduction Task?
# Initial Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Initial Dual N-Back score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "Initial Dual N-Back score")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00186# 2nd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2nd year Dual N-Back score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "2nd year Dual N-Back score")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.218# 3rd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `nback_comb_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3rd year Dual N-Back score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year Dual N-Back score")
apt_wide %>%
filter(`nback_comb_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.122Digit Span
vs BSL Grades
# Digit Span pre-degree vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Digit Span pre-degree vs. BSL grades 1st year",
y = "1st year BSL grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.122# Digit Span pre-degree vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Digit Span pre-degree vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.141# No datapoints for Digit Span @ '1 year of study'
# Digit Span 2nd year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Digit Span 2nd year vs BSL grades 2nd year",
y = "2nd year BSL grade", x = "Digit Span 2nd year")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0796vs BSL-SRT
Does Digit Span score relate to BSL-SRT score?
# Initial Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Initial Digit Span score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "Initial Digit Span score")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00124# 2nd year Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2nd year Digit Span score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "2nd year Digit Span score")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00433Barratt Impulsiveness Scale
vs BSL Grades
# Barratt Impulsiveness Scale 2nd year vs BSL grades 2nd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_bsl_2nd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Impulsivity vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "BIS 2nd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00858vs BSL-SRT
# Barratt Impulsiveness Scale 2nd year vs BSL-SRT 3rd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`bsl_srt_3rd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Impulsivity 2nd year vs. BSL-SRT 3rd year",
y = "3rd year BSL-SRT score", x = "BIS Score 2nd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.272Interpreting: predicted impact
Dual N-Back
vs Interpreting Grades
# Dual N-Back pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0123# Dual N-Back pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.108# Dual N-Back 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Dual N-Back 2nd year")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000705vs Eng>BSL interpreting
# Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.125# Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 2nd year")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.121# Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 3rd year")
apt_wide %>%
filter(`nback_comb_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.155vs BSL>Eng interpreting
# Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0605# Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 2nd year")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.178# Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = " Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 3rd year")
apt_wide %>%
filter(`nback_comb_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.103Corsi Blocks
vs Interpreting Grades
# Corsi Blocks pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Corsi Blocks pre-degree")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0114# Corsi Blocks pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Corsi Blocks pre-degree")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00282# Corsi Blocks 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Corsi Blocks 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Corsi Blocks 1st year")
apt_wide %>%
filter(`corsi_corr_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.118# Corsi Blocks 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Corsi Blocks 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Corsi Blocks 2nd year")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0791vs Eng>BSL interpreting
# Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = " Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks pre-degree")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.289# Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = " Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 2nd year")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.565# Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = " Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 3rd year")
apt_wide %>%
filter(`corsi_corr_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.132vs BSL>Eng interpreting
# Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = " Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks pre-degree")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0578# Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = " Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 2nd year")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.455# Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = " Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 3rd year")
apt_wide %>%
filter(`corsi_corr_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0169Digit Span
vs Interpreting Grades
# Digit Span pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Digit Span pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0155# Digit Span pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Digit Span pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.338# Digit Span 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Digit Span 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0709vs Eng>BSL interpreting
# Digit Span pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Digit Span pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0926# Digit Span 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Digit Span 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Digit Span 2nd year")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0851# Digit Span 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Digit Span 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Digit Span 3rd year")
apt_wide %>%
filter(`dspan_corr_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.115vs BSL>Eng interpreting
# Digit Span pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Digit Span pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00697# Digit Span 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Digit Span 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Digit Span 2nd year")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0863# Digit Span 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Digit Span 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Digit Span 3rd year")
apt_wide %>%
filter(`dspan_corr_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0264Interpreting: no predicted impact
Kirklees Sentence Reading
vs Interpreting Grades
# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.160# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point() +
theme_minimal() +
labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0931# Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading after 1 year")
apt_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0504# Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() +
labs(title = "Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading 2nd year")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0457vs Eng>BSL interpreting
# Kirklees pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Kirklees pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Kirklees pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0754# Kirklees 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Kirklees 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Kirklees 2nd year")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0193vs BSL>Eng interpreting
# Kirklees pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Kirklees pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Kirklees pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0922# Kirklees 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "Kirklees 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Kirklees 2nd year")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000130KBIT-2 Matrices
vs Interpreting Grades
# KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0419# KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0250# KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 1st year")
apt_wide %>%
filter(`kbit_acc_1st year` != "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00539# KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 2nd year")
apt_wide %>%
filter(`kbit_acc_2nd year` != "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.116vs Eng>BSL interpreting
# KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000458# KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000597vs BSL>Eng interpreting
# KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0417# KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.01722D Mental Rotation
vs Interpreting Grades
# 2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title = "2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "2D Mental Rotation pre-degree")
apt_wide %>%
filter(`mr2d_sats_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0382# 2D Mental Rotation 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2D Mental Rotation 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "2D Mental Rotation 1st year")
apt_wide %>%
filter(`mr2d_sats_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.325# 2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "2D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.150vs Eng>BSL interpreting
# 2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.198# 2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 3rd year")
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.180vs BSL>Eng interpreting
# 2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "2D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.602# 2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "2D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.3113D Mental Rotation
vs Interpreting Grades
# 3D Mental Rotation pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_1st year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3D Mental Rotation pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "3D Mental Rotation pre-degree")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0677# 3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point() +
theme_minimal() +
labs(title= "3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "3D Mental Rotation pre-degree") 
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.182# 3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "3D Mental Rotation 2nd year") 
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.261# 3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "3D Mental Rotation 3rd year") 
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.435vs Eng>BSL interpreting
# 3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation pre-degree")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.268# 3D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0974# 3D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 3rd year")
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00752vs BSL>Eng interpreting
# 3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to BSL interpreting 3rd year", x = "3D Mental Rotation pre-degree")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.475# 3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.286# 3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title = "3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.158Barratt Impulsiveness Scale
vs Interpreting Grades
# Barratt Impulsiveness Scale 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_terp_2nd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_terp_2nd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Impulsivity 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "BIS score 2nd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0000311vs Eng>BSL interpreting
# Barratt Impulsiveness Scale 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`terp_e2b_3rd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Impulsivity 2nd year vs. Eng to BSL interpreting 3rd year",
y = "2nd year Interpreting grade", x = "Eng to BSL interpreting 3rd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.115# Barratt Impulsiveness Scale 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`terp_b2e_3rd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point() +
geom_smooth(method= "lm") +
theme_minimal() +
labs(title= "Impulsivity 2nd year vs. BSL to Eng interpreting 3rd year",
y = "2nd year Interpreting grade", x = "BSL to Eng interpreting 3rd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.236vs BSL>Eng interpreting
Models
BSL Grades
What best predicts BSL grades?
( bsl_grade_mdl <- lm(grade_bsl ~ nback_comb +
mr2d_sats +
mr3d_sats +
corsi_corr +
kirk_acc,
data = apt) ) ##
## Call:
## lm(formula = grade_bsl ~ nback_comb + mr2d_sats + mr3d_sats +
## corsi_corr + kirk_acc, data = apt)
##
## Coefficients:
## (Intercept) nback_comb mr2d_sats mr3d_sats corsi_corr kirk_acc
## 193.215 -191.407 -1.222 6.259 -1.350 24.318summary(bsl_grade_mdl)##
## Call:
## lm(formula = grade_bsl ~ nback_comb + mr2d_sats + mr3d_sats +
## corsi_corr + kirk_acc, data = apt)
##
## Residuals:
## 73 77 78 81 83 85 86 88 89 95
## 8.583 -7.943 -2.078 1.933 4.007 -3.462 1.239 -5.142 5.770 -2.906
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 193.215 124.141 1.556 0.195
## nback_comb -191.407 148.647 -1.288 0.267
## mr2d_sats -1.222 3.894 -0.314 0.769
## mr3d_sats 6.259 3.153 1.985 0.118
## corsi_corr -1.350 2.861 -0.472 0.662
## kirk_acc 24.318 29.134 0.835 0.451
##
## Residual standard error: 7.788 on 4 degrees of freedom
## (122 observations deleted due to missingness)
## Multiple R-squared: 0.6388, Adjusted R-squared: 0.1873
## F-statistic: 1.415 on 5 and 4 DF, p-value: 0.3794# only fit on 10 observations
# wide version - pre-degree
( bsl_grade_mdl_2 <- lm(`grade_bsl_2nd year` ~
`nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`kirk_acc_pre-degree` +
`mlat_acc_pre-degree`,
data = apt_wide) ) ##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## `mlat_acc_pre-degree`, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_pre-degree` `mr3d_sats_pre-degree`
## 19.6328 13.1204 -0.3256
## `corsi_corr_pre-degree` `kirk_acc_pre-degree` `mlat_acc_pre-degree`
## 1.3124 20.4016 7.0848summary(bsl_grade_mdl_2)##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## `mlat_acc_pre-degree`, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.728 -4.231 1.410 5.753 20.986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.6328 69.4625 0.283 0.783
## `nback_comb_pre-degree` 13.1204 82.3773 0.159 0.876
## `mr3d_sats_pre-degree` -0.3256 2.2455 -0.145 0.887
## `corsi_corr_pre-degree` 1.3124 3.3588 0.391 0.703
## `kirk_acc_pre-degree` 20.4016 39.9823 0.510 0.620
## `mlat_acc_pre-degree` 7.0848 17.8677 0.397 0.699
##
## Residual standard error: 13.72 on 11 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.0749, Adjusted R-squared: -0.3456
## F-statistic: 0.1781 on 5 and 11 DF, p-value: 0.9652# final assessments in 3rd year
( bsl_grade_mdl_3 <- lm(`grade_bsl_2nd year` ~
`nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year`,
data = apt_wide) ) ##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`,
## data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## -43.661 89.951 4.507
## `mr2d_sats_3rd year` `corsi_corr_3rd year` `dspan_corr_3rd year`
## 6.162 5.814 -22.966summary(bsl_grade_mdl_3)##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`,
## data = apt_wide)
##
## Residuals:
## 15 17 19 20 22 23 29 31
## 0.1538 2.5860 0.4291 4.1587 -2.6070 0.9511 -3.4987 -2.1730
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -43.661 49.353 -0.885 0.4697
## `nback_comb_3rd year` 89.951 83.192 1.081 0.3926
## `mr3d_sats_3rd year` 4.507 2.526 1.784 0.2163
## `mr2d_sats_3rd year` 6.162 5.855 1.052 0.4030
## `corsi_corr_3rd year` 5.814 1.865 3.116 0.0894 .
## `dspan_corr_3rd year` -22.966 77.087 -0.298 0.7939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.942 on 2 degrees of freedom
## (25 observations deleted due to missingness)
## Multiple R-squared: 0.8976, Adjusted R-squared: 0.6414
## F-statistic: 3.504 on 5 and 2 DF, p-value: 0.2368BSL-SRT
( bsl_srt_mdl <- lm(bsl_srt ~ nback_comb +
mr3d_sats +
corsi_corr,
#kirk_acc +
#kbit_acc +
#dspan_corr,
data = apt) ) ##
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr, data = apt)
##
## Coefficients:
## (Intercept) nback_comb mr3d_sats corsi_corr
## -46.2392 75.5416 1.8857 0.4374summary(bsl_srt_mdl)##
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr, data = apt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7754 -3.2740 0.5096 2.4977 5.8657
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -46.2392 27.7600 -1.666 0.1267
## nback_comb 75.5416 34.6931 2.177 0.0545 .
## mr3d_sats 1.8857 1.1852 1.591 0.1427
## corsi_corr 0.4374 1.0153 0.431 0.6757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.009 on 10 degrees of freedom
## (118 observations deleted due to missingness)
## Multiple R-squared: 0.4738, Adjusted R-squared: 0.3159
## F-statistic: 3.001 on 3 and 10 DF, p-value: 0.08167# only being fit on 8 or 9 observations...
# wide version - pre-degree
( bsl_srt_mdl_2 <- lm(`bsl_srt_3rd year` ~
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`kirk_acc_pre-degree`,
data = apt_wide) ) ##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` +
## `kirk_acc_pre-degree`, data = apt_wide)
##
## Coefficients:
## (Intercept) `mr3d_sats_pre-degree` `corsi_corr_pre-degree`
## -6.040 1.238 1.072
## `kirk_acc_pre-degree`
## 13.862summary(bsl_srt_mdl_2)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` +
## `kirk_acc_pre-degree`, data = apt_wide)
##
## Residuals:
## 15 17 19 20 22 23 24 29 31
## -0.3363 -2.2854 -2.4169 1.0018 1.4308 2.5661 3.0885 -3.3123 0.2637
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.0398 12.7597 -0.473 0.656
## `mr3d_sats_pre-degree` 1.2379 0.9997 1.238 0.271
## `corsi_corr_pre-degree` 1.0715 0.8152 1.314 0.246
## `kirk_acc_pre-degree` 13.8625 10.3682 1.337 0.239
##
## Residual standard error: 2.877 on 5 degrees of freedom
## (24 observations deleted due to missingness)
## Multiple R-squared: 0.5861, Adjusted R-squared: 0.3377
## F-statistic: 2.36 on 3 and 5 DF, p-value: 0.1881# final assessments in 3rd year
( bsl_srt_mdl_3 <- lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year`,
#`dspan_corr_3rd year`,
data = apt_wide) ) ##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year`, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## -41.6809 45.3030 -0.2303
## `mr2d_sats_3rd year` `corsi_corr_3rd year`
## 4.2812 2.2432summary(bsl_srt_mdl_3)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year`, data = apt_wide)
##
## Residuals:
## 15 17 19 20 22 23 24 29
## 0.42204 -0.05988 -0.78030 1.07448 0.92041 0.44188 1.39317 -0.48429
## 31
## -2.92750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -41.6809 17.0093 -2.450 0.0704 .
## `nback_comb_3rd year` 45.3030 22.3562 2.026 0.1127
## `mr3d_sats_3rd year` -0.2303 0.9437 -0.244 0.8192
## `mr2d_sats_3rd year` 4.2812 1.5689 2.729 0.0525 .
## `corsi_corr_3rd year` 2.2432 0.5704 3.933 0.0171 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.853 on 4 degrees of freedom
## (24 observations deleted due to missingness)
## Multiple R-squared: 0.8627, Adjusted R-squared: 0.7253
## F-statistic: 6.282 on 4 and 4 DF, p-value: 0.0514Interpreting Grades
( terp_grade_mdl <- lm(grade_terp ~ nback_spat +
mr3d_sats +
corsi_corr +
kirk_acc +
dspan_corr,
data = apt) )##
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + dspan_corr, data = apt)
##
## Coefficients:
## (Intercept) nback_spat mr3d_sats corsi_corr kirk_acc dspan_corr
## 337.571 -311.756 6.728 -7.193 59.349 -49.499summary(terp_grade_mdl)##
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + dspan_corr, data = apt)
##
## Residuals:
## 73 77 78 81 83 85 86 88
## 4.91726 0.34380 -4.86083 2.62055 -3.46552 -2.66947 0.47671 -1.86206
## 89 95
## 4.43898 0.06057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 337.571 79.773 4.232 0.0134 *
## nback_spat -311.756 87.113 -3.579 0.0232 *
## mr3d_sats 6.728 1.562 4.306 0.0126 *
## corsi_corr -7.193 2.020 -3.560 0.0236 *
## kirk_acc 59.349 18.432 3.220 0.0323 *
## dspan_corr -49.499 21.795 -2.271 0.0856 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.933 on 4 degrees of freedom
## (122 observations deleted due to missingness)
## Multiple R-squared: 0.8833, Adjusted R-squared: 0.7374
## F-statistic: 6.056 on 5 and 4 DF, p-value: 0.05278# but is only being fit on 10 observations.....
# wide version - pre-degree
( terp_grade_mdl_2 <- lm(`grade_terp_2nd year` ~
`nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`kirk_acc_pre-degree` +
`dspan_corr_pre-degree`,
data = apt_wide) )##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## `dspan_corr_pre-degree`, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_pre-degree` `mr3d_sats_pre-degree`
## -3.6037 40.0142 1.5724
## `corsi_corr_pre-degree` `kirk_acc_pre-degree` `dspan_corr_pre-degree`
## 1.9683 -0.5881 30.7403summary(terp_grade_mdl_2)##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## `dspan_corr_pre-degree`, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.520 -1.366 1.526 2.695 10.381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.6037 52.7366 -0.068 0.947
## `nback_comb_pre-degree` 40.0142 60.4054 0.662 0.529
## `mr3d_sats_pre-degree` 1.5724 1.5528 1.013 0.345
## `corsi_corr_pre-degree` 1.9683 3.1280 0.629 0.549
## `kirk_acc_pre-degree` -0.5881 74.3808 -0.008 0.994
## `dspan_corr_pre-degree` 30.7403 63.5947 0.483 0.644
##
## Residual standard error: 9.166 on 7 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.5111, Adjusted R-squared: 0.1619
## F-statistic: 1.463 on 5 and 7 DF, p-value: 0.3116# final assessments in 3rd year
( terp_grade_mdl_3 <- lm(`grade_terp_2nd year` ~
`nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year`,
data = apt_wide) ) ##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` +
## `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` +
## `dspan_corr_3rd year`, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## -50.794 33.848 4.540
## `mr2d_sats_3rd year` `corsi_corr_3rd year` `dspan_corr_3rd year`
## 11.009 5.241 38.905summary(terp_grade_mdl_3)##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` +
## `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` +
## `dspan_corr_3rd year`, data = apt_wide)
##
## Residuals:
## 15 17 19 20 22 23 29 31
## 1.4516 -4.0283 0.5060 1.8034 -2.5138 1.3605 0.8567 0.5638
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -50.794 39.354 -1.291 0.326
## `nback_comb_3rd year` 33.848 66.338 0.510 0.661
## `mr3d_sats_3rd year` 4.540 2.014 2.254 0.153
## `mr2d_sats_3rd year` 11.009 4.668 2.358 0.142
## `corsi_corr_3rd year` 5.241 1.488 3.524 0.072 .
## `dspan_corr_3rd year` 38.905 61.470 0.633 0.592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.941 on 2 degrees of freedom
## (25 observations deleted due to missingness)
## Multiple R-squared: 0.9379, Adjusted R-squared: 0.7825
## F-statistic: 6.037 on 5 and 2 DF, p-value: 0.1482BSL>Eng interpreting
( terp_b2e_mdl <- lm(terp_b2e ~ nback_spat +
mr3d_sats +
corsi_corr +
dspan_corr,
data = apt) )##
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr, data = apt)
##
## Coefficients:
## (Intercept) nback_spat mr3d_sats corsi_corr dspan_corr
## 38.330 33.640 1.727 1.172 -13.322summary(terp_b2e_mdl)##
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr, data = apt)
##
## Residuals:
## 114 116 118 119 121 122 123 130
## -5.3638 0.7723 1.1635 2.0278 -3.5135 -2.5503 5.7516 1.7124
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.330 45.688 0.839 0.463
## nback_spat 33.640 60.825 0.553 0.619
## mr3d_sats 1.727 2.716 0.636 0.570
## corsi_corr 1.172 2.400 0.488 0.659
## dspan_corr -13.322 56.482 -0.236 0.829
##
## Residual standard error: 5.468 on 3 degrees of freedom
## (124 observations deleted due to missingness)
## Multiple R-squared: 0.3151, Adjusted R-squared: -0.5981
## F-statistic: 0.3451 on 4 and 3 DF, p-value: 0.8347# but is only being fit on 10 observations.....
# wide version - pre-degree
( terp_b2e_mdl_2 <- lm(`terp_b2e_3rd year` ~
`nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`dspan_corr_pre-degree`,
data = apt_wide) )##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree`,
## data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_pre-degree` `mr3d_sats_pre-degree`
## 911.589 -464.608 1.394
## `corsi_corr_pre-degree` `dspan_corr_pre-degree`
## 22.437 -1009.482summary(terp_b2e_mdl_2)##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree`,
## data = apt_wide)
##
## Residuals:
## ALL 5 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 911.589 NA NA NA
## `nback_comb_pre-degree` -464.608 NA NA NA
## `mr3d_sats_pre-degree` 1.394 NA NA NA
## `corsi_corr_pre-degree` 22.437 NA NA NA
## `dspan_corr_pre-degree` -1009.482 NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 4 and 0 DF, p-value: NA( terp_b2e_mdl_2 <- lm(`terp_b2e_3rd year` ~
`mr3d_sats_pre-degree`,
data = apt_wide) )##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree`, data = apt_wide)
##
## Coefficients:
## (Intercept) `mr3d_sats_pre-degree`
## 66.933 2.461# final assessments in 3rd year
( terp_b2e_mdl_3 <- lm(`terp_b2e_3rd year` ~
`nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year`,
data = apt_wide) ) ##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`,
## data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## 26.0354 57.4582 -0.4345
## `mr2d_sats_3rd year` `corsi_corr_3rd year` `dspan_corr_3rd year`
## 5.3268 1.7019 -24.8365summary(terp_b2e_mdl_3)##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`,
## data = apt_wide)
##
## Residuals:
## 15 17 19 20 22 23 24 31
## -0.50790 -0.02649 2.17263 2.19702 -1.77328 -5.94448 3.33560 0.54691
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.0354 58.8754 0.442 0.702
## `nback_comb_3rd year` 57.4582 97.3875 0.590 0.615
## `mr3d_sats_3rd year` -0.4345 3.4667 -0.125 0.912
## `mr2d_sats_3rd year` 5.3268 4.8509 1.098 0.387
## `corsi_corr_3rd year` 1.7019 2.4483 0.695 0.559
## `dspan_corr_3rd year` -24.8365 60.1614 -0.413 0.720
##
## Residual standard error: 5.464 on 2 degrees of freedom
## (25 observations deleted due to missingness)
## Multiple R-squared: 0.544, Adjusted R-squared: -0.5959
## F-statistic: 0.4773 on 5 and 2 DF, p-value: 0.7817Eng>BSL interpreting
( terp_e2b_mdl <- lm(terp_e2b ~ nback_spat +
mr3d_sats +
corsi_corr +
dspan_corr,
data = apt) )##
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr, data = apt)
##
## Coefficients:
## (Intercept) nback_spat mr3d_sats corsi_corr dspan_corr
## -59.9517 103.3147 0.6054 3.6444 16.8779summary(terp_e2b_mdl)##
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr, data = apt)
##
## Residuals:
## 114 116 118 119 121 122 123 128
## -18.1905 0.1467 -1.0444 -0.1064 -13.8652 0.3798 15.2756 6.6704
## 130
## 10.7341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -59.9517 117.6982 -0.509 0.637
## nback_spat 103.3147 154.9915 0.667 0.542
## mr3d_sats 0.6054 5.7021 0.106 0.921
## corsi_corr 3.6444 5.0795 0.717 0.513
## dspan_corr 16.8779 144.8769 0.116 0.913
##
## Residual standard error: 15.14 on 4 degrees of freedom
## (123 observations deleted due to missingness)
## Multiple R-squared: 0.2671, Adjusted R-squared: -0.4657
## F-statistic: 0.3645 on 4 and 4 DF, p-value: 0.824# but is only being fit on 10 observations.....
# wide version - pre-degree
( terp_e2b_mdl_2 <- lm(`terp_e2b_3rd year` ~
`nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`dspan_corr_pre-degree`,
data = apt_wide) )##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree`,
## data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_pre-degree` `mr3d_sats_pre-degree`
## -154.813 279.382 3.565
## `corsi_corr_pre-degree` `dspan_corr_pre-degree`
## 5.672 -46.350summary(terp_e2b_mdl_2)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree`,
## data = apt_wide)
##
## Residuals:
## 15 19 22 24 29 31
## -17.000 -3.816 2.087 12.466 1.069 5.194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -154.813 672.699 -0.230 0.856
## `nback_comb_pre-degree` 279.382 976.446 0.286 0.823
## `mr3d_sats_pre-degree` 3.565 8.503 0.419 0.747
## `corsi_corr_pre-degree` 5.672 9.890 0.573 0.669
## `dspan_corr_pre-degree` -46.350 221.553 -0.209 0.869
##
## Residual standard error: 22.17 on 1 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.5966, Adjusted R-squared: -1.017
## F-statistic: 0.3697 on 4 and 1 DF, p-value: 0.8246( terp_e2b_mdl_2 <- lm(`terp_e2b_3rd year` ~
`mr3d_sats_pre-degree`,
data = apt_wide) )##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree`, data = apt_wide)
##
## Coefficients:
## (Intercept) `mr3d_sats_pre-degree`
## 66.835 5.713# final assessments in 3rd year
( terp_e2b_mdl_3 <- lm(`terp_e2b_3rd year` ~
`nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year`,
data = apt_wide) ) ##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`,
## data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## -124.291 242.528 -7.141
## `mr2d_sats_3rd year` `corsi_corr_3rd year` `dspan_corr_3rd year`
## 21.894 7.737 -75.724summary(terp_e2b_mdl_3)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year`,
## data = apt_wide)
##
## Residuals:
## 15 17 19 20 22 23 24 29
## -0.2739 -3.3984 4.2562 3.2719 -3.5527 -11.1615 6.4241 2.0752
## 31
## 2.3591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -124.291 80.224 -1.549 0.2191
## `nback_comb_3rd year` 242.528 130.205 1.863 0.1594
## `mr3d_sats_3rd year` -7.141 4.462 -1.600 0.2079
## `mr2d_sats_3rd year` 21.894 7.566 2.894 0.0628 .
## `corsi_corr_3rd year` 7.737 3.239 2.389 0.0968 .
## `dspan_corr_3rd year` -75.724 94.760 -0.799 0.4826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.733 on 3 degrees of freedom
## (24 observations deleted due to missingness)
## Multiple R-squared: 0.8172, Adjusted R-squared: 0.5127
## F-statistic: 2.683 on 5 and 3 DF, p-value: 0.2231Factor Analysis
reduce tibble to just independent variables:
# apt <- select(apt, -session, -group, -ppt, -copy_score, -srt_score, -town_map, -bsl_grade)filter out rows with incomplete data
#apt_reduced <- filter(apt, corsi_corr != "na")
#apt_reduced <- filter(apt_reduced, nback_spat != "na")
#apt_reduced <- filter(apt_reduced, kirk_ceil != "na")reduce to just four tasks that were done at all 3 sessions
#apt_reduced <- select(apt_reduced, nback_lett:kbit_acc)FA using psych package
# fapsych <- fa(apt_reduced, nfactors=3, rotate="oblimin", scores="regression", residuals=FALSE, fm="minres")run factor analysis
# FA <- factanal(apt_reduced, factors=2, rotation="varimax", scores="regression")